Every day a whopping 2.5 exabytes of data (structured and unstructured) is created worldwide by users and enterprises, but because of its huge size, format and spread over a variety of platforms and silos, it is seldom used effectively. Enterprises need data scientists to interpret this large data into insights and solve real-life business problems, identify inefficient processes, discover potential markets to monetize, enhance data security, improve and develop customized customer services etc.
Especially as technologies like Internet of Things and cloud computing started gaining popularity there is an increasing need for professionals and specialists to crunch the huge data using machine learning tools.
Recently The European Commission has keyed the requirement for 346,000 more data scientists by 2020 with advanced computer science skills, knowledge of statistics, and domain expertise (specific to a business problem). An IBM report has predicted a demand spike by 28% by 2020 for data scientists and advanced analysts roles.
However there is some specific skill set that these roles demand from professionals, this article underlines soft skills that an individual should possess to become an effective data scientist.
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Analytics
Data scientists have to deal with a large amount of unstructured data so they need to be well equipped with knowledge of statistical tests, distributions, maximum likelihood estimators, etc. to process and conclude valuable insights. Enterprises need continuous monitoring and value propositions so they require the modelling of complex economic or growth systems to better identify valuable growth avenues.
Programming
The immense data produced daily need to be processed equally fast and efficiently, so there is no wonder developers are continuously developing tools to achieve the same. For data scientist, it is imperative to have expertise in analytical tools that are most common like SAS, Hadoop, Spark, Hive, Pig, R and etc.
They also need to have knowledge of programming languages like Python, Perl, C/C++, SQL, and Java, to help them manage unstructured data to create statistical graphs and perform basic calculations.
The demand for certain programming skills according to the percentages in which they have appeared on the job listings are as follows: with
Python (72%), R (64%), SQL (51%),
Hadoop (39%), Java (33%), SAS (30%),
Spark (27%), Matlab (20%), Hive (17%),
Tableau (14%).
Visualization & Communication
Generating insights from raw data is one thing but being able to present it in an easy to understand and effective way is another. Data scientists well equipped with knowledge and expertise in popular data visualization tools like Tableau, matplotlib, ggplot, d3.js etc. will have a relative headway.
However, for data scientists there is additional challenge of communicating their finding with their teams consisting of engineers, designers, product managers, operations etc. it is important to be an effective communicator with both technical and non-technical members of the team.
Intuitions
It is one of the skills that make the role of a data scientist most prominent, ability to perceive unobvious patterns and anticipating value in undiscovered data piles. Developing an intuition for valuable insights requires a lot of practice and years of experience, boot camps are especially quite helpful in polishing this skill.
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